TY - JOUR
T1 - UNIMODULAR SEQUENCE SET DESIGN WITH LOW WEIGHTED CORRELATION PROPERTIES
AU - Gao, Yuhang
AU - Ren, Lixiang
AU - Fan, Huayu
AU - Liu, Quanhua
AU - Mao, Erke
N1 - Publisher Copyright:
© The Institution of Engineering & Technology 2023.
PY - 2023
Y1 - 2023
N2 - In radar systems, there is a demand for the unimodular sequence set that exhibit excellent weighted correlation properties. In this paper, we propose an effective algorithm based on the memetic algorithm to minimize the weighted integrated sidelobe level (WISL) of the unimodular sequence set. The proposed algorithm takes evolutionary algorithm as the global search algorithm and majorization-minimization algorithm as the local refinement algorithm to design the unimodular sequence set. Compared to the previously proposed algorithms, this algorithm explores the solution space and improves solution accuracy more efficiently, leading to the attainment of the unimodular sequence set with significantly improved weighted correlation properties. Simulation results verify the performance of the proposed algorithm, which demonstrates that the proposed algorithm can generate the unimodular sequence set with much better weighted correlation properties compared with existing algorithms. In addition, the influence of key parameters on the performance of the proposed algorithm is investigated.
AB - In radar systems, there is a demand for the unimodular sequence set that exhibit excellent weighted correlation properties. In this paper, we propose an effective algorithm based on the memetic algorithm to minimize the weighted integrated sidelobe level (WISL) of the unimodular sequence set. The proposed algorithm takes evolutionary algorithm as the global search algorithm and majorization-minimization algorithm as the local refinement algorithm to design the unimodular sequence set. Compared to the previously proposed algorithms, this algorithm explores the solution space and improves solution accuracy more efficiently, leading to the attainment of the unimodular sequence set with significantly improved weighted correlation properties. Simulation results verify the performance of the proposed algorithm, which demonstrates that the proposed algorithm can generate the unimodular sequence set with much better weighted correlation properties compared with existing algorithms. In addition, the influence of key parameters on the performance of the proposed algorithm is investigated.
KW - CORRELATION PROPERTIES
KW - MEMETIC ALGORITHM
KW - UNIMODULAR SEQUENCE SET DESIGN
KW - WEIGHTED INTEGRATED SIDELOBE LEVEL
UR - http://www.scopus.com/inward/record.url?scp=85203151092&partnerID=8YFLogxK
U2 - 10.1049/icp.2024.1448
DO - 10.1049/icp.2024.1448
M3 - Conference article
AN - SCOPUS:85203151092
SN - 2732-4494
VL - 2023
SP - 2322
EP - 2326
JO - IET Conference Proceedings
JF - IET Conference Proceedings
IS - 47
T2 - IET International Radar Conference 2023, IRC 2023
Y2 - 3 December 2023 through 5 December 2023
ER -